The present disclosure relates generally to computer systems and in particular to in-system monitoring of multiprocessor computer systems.
Unless otherwise indicated herein, the approaches described in this section are not prior art to the claims in this application and are not admitted to be prior art by inclusion in this section.
A typical business enterprise comprises a large number of organizations (marketing, engineering, production, supply, sales, customer service, and so on). Large volumes of data are typically generated and collected by these many organizations.
Business intelligence (BI) and business warehousing (BW) tools conventionally are built on a database architecture where the data is collected and stored onto disk storage systems and subsequently read from disks (e.g., hard disk drive units) comprising the disk storage system for analysis. Conventional architectures also separate the function of transaction processing and analytical processing.
On-line transaction processing (OLTP) is typically characterized by a large number of short on-line transactions (INSERT, UPDATE, DELETE). OLTP systems in an enterprise are the source of data for the rest of the enterprise. Various organizations in an enterprise typically connect to an OLTP to record their activities. For example, a manufacturing group may connect to an OLTP system to input data into a manufacturing database such as incoming parts, production units, tracking of defects, and so on. A sales department may connect to an OLTP system to input data to a sales database.
On-line analytical processing (OLAP), by comparison, constitute a user of the data collected and stored in OLTP systems. Whereas OLTP may be viewed as a collector of raw data, OLAP may be viewed as a user of the raw data. OLAP queries are often complex and involve aggregations of the data stored in one or more OLTP databases. An OLAP database typically stores aggregated, historical data. OLAP is typically characterized by a lower volume of transactions as compared to OLTP.
There is always huge demand for real-time reporting that can leverage real-time data and provide improved decision making capability by reporting from transactional and operational systems. The success of a business may depend on how quick a reliable and smart decision can be made based on information available at that moment. Real-time computing systems have been evolving to meet these needs. One such system is based on an architecture known as in-memory computing.
In-memory computing can parse and analyze data in a matter of minutes to seconds as compared to conventional computing architectures which may require days to weeks. In-computing architectures are highly integrated systems. Maintaining and otherwise supporting such systems require equally fast response times to detect and assess changes in the system that may degrade performance.
These and other issues are addressed by embodiments of the disclosure, individually and collectively.